Master of Engineering Science
Electrical and Computer Engineering
Dr. Miriam A. M. Capretz
Applications empowered by machine learning (ML) and the Internet of Things (IoT) are changing the way people live and impacting a broad range of industries. However, creating and automating ML workflows at scale using real-world IoT data often leads to complex systems integration and production issues. Examples of challenges faced during the development of these ML applications include glue code, hidden dependencies, and data pipeline jungles. This research proposes the Machine Learning Framework for IoT data (ML4IoT), which is designed to orchestrate ML workflows to perform training and enable inference by ML models on IoT data. In the proposed framework, containerized microservices are used to automate the execution of tasks specified in ML workflows, which are defined through REST APIs. To address the problem of integrating big data tools and machine learning into a unified platform, the proposed framework enables the definition and execution of end-to-end ML workflows on large volumes of IoT data. In addition, to address the challenges of running multiple ML workflows in parallel, the ML4IoT has been designed to use container-based components that provide a convenient mechanism to enable the training and deployment of numerous ML models in parallel. Finally, to address the common production issues faced during the development of ML applications, the proposed framework used microservices architecture to bring flexibility, reusability, and extensibility to the framework. Through the experiments, we demonstrated the feasibility of the (ML4IoT), which managed to train and deploy predictive ML models in two types of IoT data. The obtained results suggested that the proposed framework can manage real-world IoT data, by providing elasticity to execute 32 ML workflows in parallel, which were used to train 128 ML models simultaneously. Also, results demonstrated that in the ML4IoT, the performance of rendering online predictions is not affected when 64 ML models are deployed concurrently to infer new information using online IoT data.
Alves, Jose Miguel, "Orchestration of machine learning workflows on Internet of Things data" (2019). Electronic Thesis and Dissertation Repository. 6150.